Data Envelop Analysis

This approach puts its focus on performance variations among companies. DEA does not make assumptions about functional forms: it is a non-parametric approach to performance evaluation.

With DEA, the efficient frontier is the benchmark against which the relative performance of firms is measured. Given a certain sample of firms, all companies should be able to operate at an optimal efficiency level which is determined by the efficient companies in the sample. These efficient companies are usually referred as the “peer firms” and determine the efficiency frontier. The companies that form the efficient frontier use minimum quantity of inputs to produce the same quantity of outputs. The distance to the efficiency frontier provides a measure for the efficiency or its lack thereof.

  • Advantages of DEA: The main advantage to this method is its ability to accommodate a multiplicity of inputs and outputs. It is also useful because it takes into consideration returns to scale in calculating efficiency, allowing for the concept of increasing or decreasing efficiency based on size and output levels.
  • Disadvantages of DEA: The results are potentially sensitive to the selection of inputs and outputs, so their relative importance needs to be analyzed prior to the calculation. However, there is no way to test their appropriateness. The number of efficient firms on the frontier tends to increase with the number of inputs and output variables. When there is no relationship between explanatory factors (within inputs and/or within outputs), DEA views each company as unique and fully efficient and efficient scores are very close to 1, which results in a loss of discriminatory power of the method.


      An example of an analysis using DEA (and other methods) in the assessment of the efficiency of private and public utilities in Africa

For more details on this study, see

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